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NMR-based metabolomics and fluxomics: developmentsand future prospects
Patrick Giraudeau
To cite this version:Patrick Giraudeau. NMR-based metabolomics and fluxomics: developments and future prospects.Analyst, Royal Society of Chemistry, 2020, �10.1039/D0AN00142B�. �hal-02507869�
NMR-based metabolomics and fluxomics:
developments and future prospects
Patrick Giraudeaua,*
a Université de Nantes, CNRS, CEISAM UMR 6230, F-44000 Nantes, France
* Correspondence to: [email protected]
Abstract
NMR spectroscopy is an essential analytical technique in metabolomics and fluxomics
workflows, owing to its high structural elucidation capabilities combined with its intrinsic
quantitative nature. However, routine NMR “omic” analytical methods suffer from several
drawbacks that may have limited its use as a tool of choice, in particular when compared to
another widely used technique, mass spectrometry. This review describes, in a critical and
perspective discussion, how some of the most recent developments emerging from the NMR
community could act as real game changers for metabolomics and fluxomics in the near future.
Advanced developments to make NMR metabolomics more resolutive, more sensitive and
more accessible are described, as well as new approaches to improve the identification of
biomarkers. We hope that this review will convince a broad end-users community of the
increasing role of NMR in the “omic” world at the beginning of the 2020s.
Keywords
Metabolomics; Fluxomics; NMR spectroscopy; Resolution; Sensitivity; Accessibility;
Structure elucidation
Introduction
In the family of “omic sciences”, metabolomics and fluxomics represent one of the most
exciting challenges that the analytical chemists ever had to face. Metabolomics deals with the
measurement (identification and quantification) of the largest possible number of metabolites
in a broad variety of biological systems, including cells, biofluids and tissues from plant, animal
or human origin.1 Fluxomics aims at obtaining information on metabolic fluxes, ie. on the rate
of metabolic conversions in such systems.2 Both metabolomics and fluxomics deal with a great
diversity of small molecules with molecular weight typically lower than 1000 Da, such as amino
acids, carboxylic acids, carbohydrates, alcohols, amines, lipids, more complex molecules such
as specialized metabolites, and even drugs and their degradation products. Since metabolites
are the final downstream products of genomic, transcriptomic, and/or proteomic perturbations,
their measurement brings critical insights into systems biology, making it possible to better
characterize and understand biological mechanisms, but also to identify biomarkers of a
pathological state or to classify sample groups depending on their origin.
Metabolomics and fluxomics methods actually include several complementary approaches.3 On
the one hand, untargeted metabolomics focuses on the measurement and comparison of all
detectable signals in a series of samples from different groups, followed by the assignment of
relevant signals to metabolite structures, focusing on signals whose variation across sample
groups is statistically different. On the other hand, targeted methods focus on the accurate and
precise quantitation of a well-defined set of known metabolites. Between untargeted and
targeted methods, some approaches are termed “semi-targeted” when they focus on a specific
compound class, e.g. sugars, polar metabolites, etc. Fluxomics also focuses on small molecules,
but involves isotopically labeled compounds which are used as tracers to determine the
fluxome, ie. the complete set of metabolic fluxes in a living organism. The typical fluxomics
approach consists in introducing a 13C-labeled precursor into the biological system, followed
by an accurate measurement of the level of incorporation of 13C into metabolites.4
Metabolomics and fluxomics involve well-defined workflows that include successive steps
requiring complementary scientific expertise. A tailored design of the biological experiment is
required to accurately answer a given biological question, and this requires the combined
expertise of biologists and analytical chemists. Generating analytical data on biological samples
requires the expertise of NMR or MS specialists, while the expertise of biostatisticians is often
indispensable to exploit the resulting data.
NMR and MS are from far the most widely used methods for metabolomics studies. The
advantages and drawbacks of the two methods for the study of complex metabolite mixtures
have been extensively reviewed.5-8 They are often summarized by a better reproducibility and
a more reliable metabolite structure identification for NMR, versus a much higher sensitivity
for MS, although this is certainly a reductive judgment from both sides. In fact, the two
techniques are highly complementary, and an increasing number of metabolomics studies have
reported the combined used of MS and NMR, either to improve metabolite identification or
even in combined multi-platform data integration strategies to improve group classification. In
fluxomics, the complementarity between the two techniques is even stronger. While MS
provides sensitive information on the fractional enrichment of mass isotopomers, NMR
provides detailed positional information on isotope enrichments.9 For all these reasons, an
increasing number of analytical platforms report the joint use of NMR and MS on a routine
basis.
In spite of this complementarity, the proportion of MS-based experiments in metabolomics has
increased much faster than the proportion of those relying on NMR, over the last decade.10
There are certainly multiple and complex reasons explaining this situation, such as the easier
accessibility to MS instruments, and the higher associated sensitivity. But intriguingly, the last
20 years have also witnessed tremendous developments in liquid-state NMR spectroscopy,
which have been little applied to metabolomics and fluxomics, although these fields would
highly benefit from the new tools that the NMR community has been developing for the analysis
of mixtures. Indeed, the vast majority of routine NMR metabolomics analyses rely on 1D 1H
pulse sequences with solvent signal suppression schemes.11 A limited number of 2D
experiments are also used to help with structural elucidation,12 and also in fluxomics to facilitate
the measurement of positional 13C isotopic enrichments.13 But most of the recent developments
which have been driving the small molecule NMR community for the last 20 years (e.g. fast
2D methods, pure-shift spectroscopy, hyperpolarization, etc.) are not part of the daily arsenal
in NMR metabolomics. One of the possible reasons lies in the limited connections between the
historical NMR groups who have been driving the field for 50 years and the large community
of NMR users who are involved in practical metabolomics studies. Unlike the MS community,
who has devoted lots of efforts to the development of “omics” sciences, most NMR
spectroscopy groups have rather been focusing on applications in the fields of structural biology
or material sciences. And the proportion of presentations on metabolomics and fluxomics is
still quite low at NMR conferences.
Fortunately, this paradigm seems to be rapidly changing at the beginning of the 2020s. On the
one hand, NMR methodology groups have realized that metabolomics and fluxomics provide a
great diversity of complex samples that offer considerable and exciting spectroscopic
challenges in terms of concentration, dynamic range and peak separation. On the other hand,
metabolomics and fluxomics groups are increasingly aware of the resolution and sensitivity
boost offered by new NMR developments. Eventually, an increasing number of research groups
involve joint expertise of NMR spectroscopists and omics experts. Rather than being a
comprehensive literature review, the present contribution aims at highlighting how emerging
NMR methods currently act as a game changer for metabolomics and fluxomics, by being
capable of meeting the most exciting challenges raised by a demanding end-users community.
Metabolomics and fluxomics are discussed in parallel rather than separately, since they share a
number of common features in terms of studied samples and analytical challenges.
The following challenges –summarized in Figure 1– will be addressed in this review, focusing
on how recent NMR advances enabled significant improvements for the analysis of
metabolomics samples: peak overlap, low sensitivity, limited accessibility and difficulty in
biomarker identification. Challenges pertaining to other parts of the metabolomics and
fluxomics workflow (i.e., sample preparation, statistical analysis), while equally important, will
not be addressed in details. Regarding peak overlap, we will describe how recent advances in
multi-nuclear, multi-pulse and multi-dimensional NMR offer appealing solutions to disentangle
overlapping peak resonances, thus making the analysis of metabolomics and fluxomics data
easier, less ambiguous and more accurate. We will also describe solutions based on physical
and chemical methods to simplify NMR spectra of complex mixtures. We will then discuss how
recent sensitivity improvements based on higher magnetic fields, better probes, and
hyperpolarization, have significantly reinforced the role of NMR. The accessibility challenge
will be discussed in light of the recent advances in the development of benchtop NMR
hardware, which offers promising performance for profiling applications on complex samples.
Finally, we will attempt to illustrate how processing developments –alone or combined with
MS methods– have facilitated the identification of relevant biomarkers from NMR spectra of
complex metabolomics and fluxomics samples. Note that this review focuses on high-
throughput, in vitro analysis of biological sample collections, and that in vivo analysis is left
out of the scope of the discussion, although this field will also certainly benefit from the
developments described here.
Figure 1. Overview of the current challenges in NMR-based metabolomics and fluxomics, and of the
main solutions being explored by the research community.
Challenges
Peak overlap
Sensitivity
AccessibilityBiomarker
identification
i
Pure-shift
2D
Selectivemethods
Hyperpolarization
High fields
Probes
Benchtop
Correlation /ratiomethods
Combinationwith MS
Towards a better separation of metabolite signals
Commonly studied samples in metabolomics, such as biofluids and extracts, can be qualified
as “complex samples” from the analytical point of view. They contain a large diversity of
metabolites, and although NMR can “only” detect a few hundreds of them, the corresponding
signals are most often heavily overlapped. The resulting spectral complexity is further
compounded by the strong solvent peak(s) – which can be efficiently suppressed with
appropriate methods.14 Overall, the routine metabolomics workflow still suffers from
ubiquitous peak overlaps that make the identification or quantification of metabolites
ambiguous. In fluxomics, the overlap between peaks is even further complicated by the
complexity of 13C isotopic patterns. Signal processing methods have been developed to
deconvolute overlapping metabolite signals, both in the case of metabolomics15-17 and
fluxomics.18 However, these approaches often rely on prior information on the metabolite
resonances, and the corresponding databases are often specific of a given biological matrix.
Moreover, deconvolution methods may fail when peak overlap is too strong.19
In order to deal with this drawback, this section highlights how NMR metabolomics and
fluxomics have recently benefited from emerging NMR methods which have been developed
to disentangle overlapping resonances in small molecule mixtures, and are now increasingly
applied to “real-life” omic studies.
Heteronuclear 1D NMR spectroscopy
A first strategy to deal with overlapping peaks in complex mixtures of metabolites is to rely on
alternative nuclei. In the case of metabolites, 13C is particularly relevant since it is present in
virtually all metabolites, and offers a much larger frequency range than 1H, leading to reduced
overlap. Unfortunately, 13C NMR is also much less sensitive than 1H NMR, owing to its lower
magnetogyric ratio (ca. ¼ of the proton value) and to a low natural abundance (1.1%). Still,
metabolomics studies can benefit from direct 13C detection at natural abundance in the case of
concentrated samples such as in food sciences. For instance, 13C NMR profiling has been
successfully applied to the classification of coffee beans20 or olive oil.21 The development of
more sensitive NMR probes also allowed the acquisition of natural abundance 13C spectra on
biofluids.22, 23 Strategies to enhance the sensitivity of 13C NMR profiling based on polarization
transfer methods have also been successfully implemented.24 In this case, only relative
measurements are possible due to the peak-specific coefficient of proportionality between the
NMR signal and the corresponding metabolite concentration. Another approach to enhance the
sensitivity of 13C NMR detection is to rely on 13C-enrichment of the biological material. Of
course, such enrichment forms the basis of 13C Metabolic Flux Analysis (MFA) or fluxomics,
as it provides crucial information on the incorporation of labeled carbons by biological systems,
ie. on metabolic pathways.2
Multi-dimensional NMR
Multi-dimensional NMR methods, and particularly 2D NMR, are often used to facilitate the
attribution of peaks and to achieve structure elucidation. Indeed, 2D experiments offer the
advantage of spreading overlapped peaks along two orthogonal dimensions, thus limiting peak
overlap while providing additional information on chemical structures.25 Moreover, the great
diversity of multi-dimensional pulse sequences makes it possible to choose the best compromise
between sensitivity, rapidity and peak separation. The typical 2D NMR experiments used in
metabolomics are J-resolved spectroscopy, homonuclear 2D correlation experiments such as
TOCSY (total correlation spectroscopy) or heteronuclear 2D correlation experiments such as
HSQC (heteronuclear single-quantum correlation).26 However, these experiments are generally
recorded on a small subset of samples from a given study. Moreover, they are mostly used for
peak identification and the information on peak volumes is often not exploited. The situation is
slightly different in fluxomics, where 2D experiments have become part of the daily arsenal to
determine position-specific isotopic enrichments, from TOCSY or HSQC experiments.13
The main reason why the use of multi-dimensional NMR is still not as widespread as it could
be is the long experiment time required to record such spectra with a sufficient resolution and
sensitivity.27 For instance, 2D experiments typically need the repetition of several hundreds of
1D experiments, leading to experiment times between a few tens of minutes and several hours.
Such durations are often not compatible with the high-throughput character required when
analyzing large sample collections such as those typically encountered in metabolomics and
fluxomics studies. Fortunately, the NMR community has developed a great variety of methods
to accelerate multi-dimensional experiments.28 These methods include fast repetition
techniques,29 spectral aliasing,30 non-uniform sampling (NUS)31 of the indirect dimension(s) or
less conventional methods such as Hadamard32 or Ultrafast (UF)33 spectroscopies. It is only
recently that some of these approaches have reached a sufficient level of maturity to be applied
to metabolomics studies.25 Not only they are compatible with high-throughput studies, but it
has been shown –at least in the case of UF NMR– that under certain conditions, fast acquisitions
offer a higher repeatability than conventional 2D NMR since they are less sensitive to hardware
instabilities.34
The following paragraphs describe recent examples highlighting the potential of such rapid 2D
NMR acquisitions for untargeted and targeted metabolomics, and for fluxomics as well. Figure
2 illustrates some of these approaches in the case of UF 2D NMR, which has been chosen as an
example since it has reached a sufficient level of maturity to be applied to these three research
areas.35 Note that the principles of UF 2D NMR –which relies on a spatial encoding of the
sample thanks to the combination of chirp pulses with magnetic field gradients– will not be
described here but have been extensively reviewed in recent literature.35, 36 It is also fair to
mention that UF 2D NMR suffers from a well-known sensitivity penalty compared to
conventional NMR,36 which explains why UF 2D NMR is best suited to relatively concentrated
metabolite samples such as extracts.
Figure 2. Illustration of the potential of fast 2D NMR methods based on ultrafast 2D NMR (COSY in
these examples) in metabolomics and fluxomics. (Top) Untargeted lipidomics performed by fast 2D
COSY (30 min at 700 MHz) on pig lipid serum extracts efficiently separates samples from pigs treated
with a growth promoter (ractopamine) versus control pigs.37 (Middle) Targeted quantification
combining fast 2D COSY (5 min at 700 MHz) with a calibration approach accurately determines the
concentration of metabolites with overlapped peaks in tomato extracts.38 (Bottom) Fast 2D COSY (3
min at 400 MHz) with 13C-decoupling in the F2 dimension applied to 13C-enriched E. Coli. cell
extracts makes it possible to measure position-specific isotope enrichments with a 1-2% accuracy.39
In the case of untargeted analyses, 2D NMR could in principle be used as routine data
acquisition tool, in addition to (or in replacement of) 1D spectra. Several studies have now
demonstrated the input of using 2D NMR in the untargeted metabolomics workflow. One of
the first papers along this direction was published by Van et al., who reported a higher
performance of 2D TOCSY with zero-quantum filtering, versus 1D 1H NMR for metabolic
profiling of urine sample from mice.40 They nicely demonstrated that statistical models obtained
UF COSY, tomato polar extract
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UF COSY, pig serum lipid extract Bucketing Classification and biomarker identification
Control Treated30 min, 700 MHz cryo
5 min, 700 MHz cryo
TBG
AAB
LAB
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DBA+YBA+FBAKED + RDG
EGBVBG
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PGD
EBG
ABA
TGB
+
UF COSY, E. Coli cell extract
EAB
LAB
Isotope profile analysis Position-specific isotope enrichments
41.2%
50.7%
MET
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3 min, 400 MHz
from 2D spectra were more efficient than those obtain from 1D data to characterize statistically
relevant changes in low abundance metabolites. However, the experiment duration associated
with 2D spectra was extremely long (17 hours per spectrum based on the paper’s experimental
parameters) and not suited to routine analysis. Later on, two studies demonstrated efficient data
processing strategies to highlight statistically relevant biomarkers from 2D spectra, either based
on pattern recognition41 or on image processing methods.42 However, experiments remained
limited by their long duration, but the situation started to change ten years ago with the use of
fast acquisition methods. In 2009, Ludwig et al. reported the use of Hadamard spectroscopy for
untargeted metabolomics of colorectal cancer.43 In 2014, Le Guennec et al. investigated –on
model samples– the impact of time-saving strategies such as NUS or UF, associated with
classical bucketing data processing strategies.44 Their results showed that 2D spectra –including
fast methods– provided a similar group separation compared to 1D data, but a much less
ambiguous biomarker identification, that was attributed to a better peak separation. Féraud et
al. reached a similar conclusion on 2D COSY data recorded on human urine samples, showing
that 2D spectra provided a higher level of clustering after statistical analysis.45 In 2018,
Marchand et al. applied fast 2D approaches (UF COSY and NUS TOCSY) on pig serum lipid
extracts, to address chemical food safety issues associated with the administration of a growth
promoter, ractopamine37 (Figure 2a). The results showed that fast 2D methods provided the
same quality of clustering as 1D NMR, with no major time penalty. Moreover, 2D spectroscopy
allowed a less ambiguous identification of biomarkers, again resulting from a better spreading
of overlapped resonances. These results show that fast 2D NMR methods have reached a
sufficient level of maturity to be applied in the routine untargeted metabolomics workflow.
However, there are still limitations to their adoption by a large community, such as the lack of
automated 2D bucketing tools, or –in the case of UF 2D NMR– the time and expertise needed
to implement the method on a spectrometer.
Fast 2D NMR methods also appear to be very promising for targeted quantitative metabolomics.
When one needs to accurately determine the concentration of targeted analytes in complex
mixtures, 2D NMR provides an appealing solution to the peak overlap issue. However, 2D
NMR pulse sequences do not provide immediate quantitative information contrary to 1D NMR.
Indeed, as in 1D NMR, the signal (peak volume) is proportional to concentration, but the
coefficient of proportionality is different for each peak, owing to the multi-pulse nature of 2D
pulse sequences. Several strategies have been considered to circumvent this limitation.46 The
first one consists in calibrating the response factor of each peak of interest (at least one per
targeted metabolite) by external calibration or standard additions.47, 48 This procedure can lead
to accurate quantification (ca. 1-2%), and multiple peaks can be calibrated simultaneously by
carefully designing a single series of calibration mixtures containing all the targeted analytes in
known concentration. However, it requires that the analytes are available as commercial
standards of known purity. This is the case for most primary metabolites, but may be more
problematic for specialized metabolites. An alternative consists in designing specific 2D NMR
pulse sequences where the coefficient that correlates the concentration with peak volumes is
approximately the same for each peak. Such performance has been reached so far for the HSQC
pulse sequence, thanks to a variety of methods that compensate for the impact of J-couplings
on peak volumes.49-52 These methods make it possible to quantify multiple analytes from 2D
spectra using a single internal reference, exactly like in 1D NMR. However, most of them are
less accurate as they do not compensate for differences in transverse relaxation times between
analytes. An exception is the HSQC0 method, however it requires long experiment times (3
spectra for each sample) which are not really compatible with high-throughput metabolomics.52
These various targeted quantitative approaches have already been successfully applied to a
broad diversity of samples and studies. In most cases, fast 2D experiments were crucial to
ensure that the method would be applicable in routine, but also to limit the impact of the
spectrometer variability in the course of the experiment. In 2012, Martineau et al. applied a
homonuclear double-quantum experiment with optimized experimental parameters to
determine the concentration of multiple major metabolites with a standard addition approach.48
Similar results on the same biological matrix were obtained by Le Guennec et al. with a UF
COSY experiment. Later on, Jézéquel et al. applied UF COSY with an external calibration
method to accurately quantify major metabolites in polar extracts of tomato fruit.38 (Figure 2b).
Other recent applications of quantitative 2D NMR with calibration strategies or standard
additions include the concentration determination of cyclodextrins in blood plasma7 or of
taurine in energy drinks.53 As for direct quantitative HSQC methods, they have also been
successfully applied to solve various quantification issues, such as the concentration
determination of sugar phosphates in plants8 or the quantification of natural products in herbal
supplements.19 The latter example is particularly interesting, since it provides an example where
1D peak overlap is so high that deconvolution approaches fail, thus justifying the need for
quantitative 2D NMR methods.
Fast 2D NMR methods have also found applications in the field of fluxomics, where 2D NMR
was already used on a regular basis for the determination of position-specific isotope
enrichments. Homonuclear UF COSY and UF TOCSY pulse sequences allowed the accurate
determination of such enrichments in E. Coli extracts within 3 minutes only (versus several
hours for the conventional experiment).39 A fast heteronuclear J-resolved experiment was also
designed for the same purpose, both in a conventional54 and ultrafast version.55 Eventually, both
approaches were combined with a fast 3D acquisition scheme capable of providing an excellent
separation between overlapped metabolite peaks in an UF 2D COSY plane, while retaining the
isotope enrichment information in an orthogonal dimension.56 A few minutes only were
required to record the 3D spectrum, while hours would have been needed to reach the same
result with conventional acquisition strategies. More recently, a fast repetition 2D HSQC
method (the ALSOFAST-HSQC) was applied to study the impact of antioxidant gold
nanoparticles on cancer cells grown on a 13C glucose-enriched medium. Within 30 minutes, a
highly resolved HSQC spectrum was obtained, showing characteristic isotope patterns for each
13C position, opening interesting perspectives for fluxomics.57
The above-mentioned examples highlight the potential of fast 2D NMR acquisition strategies
in various areas of metabolomics and fluxomics. At the time of writing, these methods are still
not used on a routine basis in most research laboratories. Communication efforts are necessary
to make end-users aware of recent methodological advances, as well as efforts to automate the
acquisition and processing of fast 2D spectra. Finally, developments are still very active in this
field and one should also pay attention to recently developed alternative fast 2D methods, such
as absolute minimum sampling, which have not yet been applied to full metabolomics studies
but have shown promising results on complex mixtures.58 Another interesting time-saving
strategy was also recently suggested for 1D NMR, which consists in shortening longitudinal
relaxation times by adding a paramagnetic co-solute.59 This approach could potentially be
combined with the fast 2D experiments mentioned above.
Pure-shift NMR
While the methods described so far aimed at improving the separation between overlapped
peaks, pure-shift NMR methods focus on the removal of homonuclear couplings on 1H spectra
to turn all multiplets into singlets.60, 61 Different strategies have been described, such as those
relying on simultaneous spectral and spatial selection, or on BIRD (bilinear rotation
decoupling) building blocks. Those approaches are applicable both in 1D and 2D NMR and
have the potential to greatly simplify the assignment and quantification of biomarkers in
metabolomics.
However, a major drawback of 1D pure-shift NMR method is that they suffer from low
sensitivity –a few percent of the one from conventional 1D experiments. A second limitation is
that they may be hampered by artefacts due to the data chunking mode used during acquisition,
and also due to imperfect decoupling in the case of strongly coupled systems. This may explain
why the application of 1D pure-shift NMR to metabolomics has been quite limited so far.
However, the latter limitation was recently circumvented thanks to the SAPPHIRE-PSYCHE
approach which is able to deliver “ultraclean” 1D pure-shift 1H spectra almost free of
artefacts.62 Based on this approach, Lopez et al. recently demonstrated the very first successful
application of 1D pure-shift NMR to a real metabolomics study on Physalis Peruviana fruit
extracts from different Andean ecosystems.63 Figure 3 illustrates how this optimized
SAPPHIRE-PSYCHE method leads to nicely homodecoupled 1H spectra while leading to much
cleaner spectra than the original PSYCHE method. In this study, the spectra were processed
with statistical analysis and compared to those obtained from conventional 1D 1H NMR data.
The PLS separation between sample groups was found comparable between conventional and
pure-shift 1H NMR, but the biomarker identification based on STOCSY analysis of the NMR
data was improved with the pure-shift approach, leading to a much less ambiguous
identification of biomarkers. While the approach is limited in terms of sensitivity, the spectra
obtained with the SAPPHIRE-PSYCHE method are much less prone to artefacts than other
pure-shift methods, and this result certainly opens nice perspectives for the application of pure-
shift NMR to metabolomics. Another recent study should be noted, which reports the use of
PSYCHE NMR with statistical analysis to detect adulteration of honey and to assess the
geographical origin of tea.64 However, the results were less convincing compared to
conventional 1D NMR, probably because the pulse sequence did not include the SAPPHIRE
module, which further justifies the potential impact of this recent methodological advance.
Figure 3. Selected expansion regions of 1H NMR (1H), PSYCHE (P), and SAPPHIRE (S) spectra of
an aqueous extract of Cape gooseberry (Bambamarca I) showing signal assignments. Figure
reproduced from Ref. 63 under Creative Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/)
While 1D pure-shift NMR experiments are sensitivity-limited, this is not the case of
heteronuclear 1H-13C 2D experiments where pure-shift spectra can be obtained in the 1H
dimension at no cost in terms of sensitivity, and with a substantial gain in resolution. Such a
pure-shift 2D HSQC approach was in fact included in the QUIPU approach, already mentioned
in the previous section, which was successfully applied to various targeted quantitative
studies.19, 51, 65 In 2019, Timári et al. suggested that the pure-shift 2D approach could be relevant
for untargeted metabolomics;66 application to a real untargeted metabolomics study could be
expected in the near future. Finally, the first application of pure-shift NMR to the field of
fluxomics has been reported very recently by Sinnaeve et al., who developed a pure-shift 2D
heteronuclear J-resolved experiment to extract position-specific 13C enrichments in heavily
overlapped systems.67
Since all the studies involving pure-shift NMR in metabolomics and fluxomics are less than 2
years old at the time of writing, one could anticipate that pure-shift NMR will certainly find
many successful applications in the field, particularly for samples offering sufficient metabolite
concentrations. Together with the 2D NMR developments mentioned previously, this forms the
demonstration that pulse sequence NMR developments have much to offer to the field, and that
metabolomics and fluxomics would highly benefit from stronger interactions with the NMR
methodology community.
Towards more selective NMR experiments
While the methods described in the previous section aimed at improving the separation between
signals from all detectable metabolites in a mixture, an alternative is to reduce the number of
observable analytes in order to yield simpler spectra. This approach may seem paradoxal in
metabolomics, which by essence aims at detecting a maximum number of signals. However,
when targeted information is sought, for instance on a specific class of molecules, or on
molecules with specific properties, selective methods can be an efficient way of discriminating
certain metabolite classes. While this strategy may be seen as a loss of universality of the NMR
detector, it actually makes NMR closer to MS, which is by essence a selective method,
particularly when coupled to chromatography.
Molecule-selective pulse sequences
A first strategy along this line is to rely on pulse sequence capable of filtering out the signal
from certain classes of molecules. In the case of biofluids, a widely used method consists in
using a CPMG (Carr-Purcell-Meiboom-Gill) pulse sequence before detection –possibly
combined with a water presaturation scheme– so that the signals from large molecules (e.g.,
Proteins) are eliminated through T2 relaxation during the pulse sequence.68 This leads to a flatter
baseline and enhances the contribution from smaller molecules. On the contrary, diffusion-
based filters make it possible to filter out the signals from fast-diffusing molecules (metabolites)
while those from slow-diffusing molecules (lipids, proteins) can be observed.69 Recent
examples in the literature highlight the complementarity of those methods in a variety of
situations.29, 70 Concerning the diffusion-edited experiment, it is worth highlighting the 2D
DOSY (diffusion-ordered spectroscopy) experiment, which has the potential to virtually
separate signals from mixture components based on their diffusion coefficients.71 However, this
method has been little applied in metabolomics studies, apart from examples where it was used
for the identification of metabolites with overlapping signals.72, 73
Physical and Chemical methods for spectral simplification
Several approaches have been described that rely on physical of chemical discrimination of
metabolite signals. The most obvious approach is to rely on the physical separation of mixture
components through high-performance liquid chromatography (HPLC) prior to NMR
detection.74 While regularly used in natural product chemistry for the identification of unknown
metabolites, this approach has been of limited use in metabolomics, probably because it is not
compatible with high-throughput analysis, but also because it is a dilutive technique associated
with solvent gradients which are detrimental to the quality of NMR spectra.
Other recent developments –mainly driven by the group of Bruschweiler– relies on selective
interaction between metabolites and charged silica nanoparticles.75 Such nanoparticles are
added to the NMR sample, and metabolites that bind to the nanoparticles experience strong line
broadening. This leads to the selective suppression of the NMR signals of metabolites whose
charge is opposite to the charge of the nanoparticles. Depending on the cationic or anionic
nature of the nanoparticles, one can finely tune the resulting interaction, leading to the
extinction of specific signals. Salvia et al. suggested an interesting complementary approach
which consists in specifically targeting metabolites of interest by coating nanoparticles with
ligands that would selectively bind to them.76 These “chemosensing” approaches leads to a
spectral simplification that can be beneficial when spectra are overcrowded. Figure 4 shows
how this method can significantly simplify 2D HSQC spectra of urine samples. On a side note,
such nanoparticles can also be used at the sample preparation stage, prior to the NMR detection,
to remove the proteins from serum samples.75 While these methods appear promising, their
application to real case studies in metabolomics or fluxomics has not been demonstrated yet.
Figure 4. 1D 1H and 2D 13C–1H HSQC spectra of 10-compound model mixture (a) without and (b)
with anionic silica nanoparticles (SNPs). Red and blue squares highlight the cross-peaks that are
suppressed by the presence of SNPs. Reprinted with permission from Ref. 75. Copyright 2016
American Chemical Society
Towards more sensitive NMR metabolomics
Magnets and probes
The low sensitivity of NMR is certainly the major bottleneck for its broad application in the
field of metabolomics, particularly when compared to MS. NMR is inherently affected by the
weak nuclear polarization. For instance, at a 14 T magnetic field operating at 300 K –the typical
configuration for metabolomics– the polarization of 1H is only 0.000008. This leads to limits
of detection in the µM range, which is good enough for the detection of primary metabolites in
biofluids, but not adapted to the detection of specialized metabolites in plant samples, for
instance.
Fortunately, numerous recent developments have paved the way towards more sensitive NMR
metabolomics and fluxomics, announcing a better complementarity between NMR and MS in
the near future. A straightforward –but technically challenging– approach consists in increasing
the magnetic field. Traditional NMR metabolomics experiment are performed at a 1H Larmor
frequency between 500 and 800 MHz, but commercial magnets are now available at up to 1.2
GHz,77 whose potential for metabolomics remains to be explored –although preliminary spectra
on biofluids have been recently reported63. Since the NMR sensitivity scales with B03/2, a
sensitivity gain of ca. 2.8 can be obtained by switching from 600 MHz to 1.2 GHz, which may
seem useless when considering the price difference (a factor of ca. 15 between the two
equipments at the time of writing). However, this magnetic field increase would in principle
translate to a considerable 7.8 gain in experiment time –a game changer for high-throughput
applications. In addition, higher magnetic fields also come with an improvement in spectral
dispersion that can help to better separate overlapping signals. The impact of very high
magnetic fields for routine metabolomics or fluxomics has not been systematically evaluated
yet, but preliminary data at very high field5 (Figure 5) highlight their potential to detect small
signals from metabolites in overcrowded regions.
An alternative technological approach to improve sensitivity is the development of more
sensitive probes that help maximizing the signal-to-noise ratio (SNR). This is the case of
cryogenically cooled probes, capable of improving the SNR by a factor of 3 to 4.78 However,
such probes are not well suited to samples with a high salinity, such as extracts dissolved in a
buffer, as is often the case in plant metabolomics.79 Alternatively, microprobes have been
designed to maximize the sensitivity for mass-limited samples. For instance, a 1.5 mm high
temperature superconducting probe has been developed for 13C NMR metabolomics at natural
abundance, which was successfully applied to Drosophila melanogaster extracts and mouse
serum.23 Microprobes have also been developed under HR-MAS (high-resolution magic angle
spinning) configurations, in order to work on small amounts of tissue samples.80
Still with the aim to pursue the quest for a sensitive NMR detection of metabolites in mass-
limited samples, recent developments in the field of microfluidics seem particularly promising.
For instance, Utz and co-workers recently developed a system that can detect metabolites at
sub-millimolar concentrations in sample volumes of a few µL only.81 Such methods open very
promising perspectives for analyses on very small samples –they could even make NMR a tool
of choice for the emerging domains of single-cell metabolomics and fluxomics.82
Figure 5. Demonstration of magnetic field strength and probe specificity on spectral
resolution of bovine serum recorded with the same parameter set on three spectrometers
working at 500, 700, and 950 MHz proton frequencies at 25 °C. Figure reproduced from Ref.5
under Creative Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/)
Hyperpolarization
While the microprobe and microfluidics strategies mentioned above achieve an impressive
performance in reducing the sample mass needed for NMR metabolomics and fluxomics, they
do not act much on the limit of detection in terms of molar concentration. Such a goal could be
reached in a near future through the application of hyperpolarization methods that can enhance
the NMR sensitivity by up to 4 orders of magnitude by drastically enhancing the nuclear
polarization. Among the hyperpolarization methods, two techniques have been recently applied
to samples with metabolomics or fluxomics relevance. The first approach is the use of para-
hydrogen to transfer the transfer of hyperpolarization from H2 in the para state to the nuclear
spins of analytes. The most general implementation of this method is the SABRE technique
(signal amplification by reversible exchange) where an iridium-based metal complex is used to
transfer the hyperpolarization to the analytes in a reversible fashion.83 While this method is
limited to metabolites that can bind to this iridum catalyst –although attempts have been made
to make it more versatile84– it is relatively simple to implement, and the reversible interaction
makes the approach compatible with the acquisition of multi-dimensional experiments.85 Under
certain conditions, the SABRE method can even be used for quantitative analysis when
combined with a standard addition method.86 This approach was successfully applied by Tessari
et al. to quantify analytes at low micromolar concentrations in natural extracts.87 It should be
noted that the short lifetimes of SABRE-enhanced signals make the use of multidimensional
cumbersome, but this drawback can be circumvented by combining it with UF 2D NMR 88, 89
or by relying on flow or shuttling systems that enable multi-scan experiments.85, 90
A second approach is the use of dissolution dynamic nuclear polarization (D-DNP), where the
sample is mixed with free radicals in a solution that forms a glass upon freezing at 1-2 K.91, 92
Under such a glassy state, the polarization can be transferred from electrons to nuclei by
irradiating the sample with microwaves. The frozen sample can then be quickly transferred to
a liquid-state NMR spectrometer where signals enhanced by several orders of magnitude
compared to a classical NMR experiment can be obtained. This approach is very general, since
it can in principle enhance the signal of all metabolites in a mixture. However, it is technically
demanding as it requires specific and expensive hardware in addition to the NMR magnet.
Moreover, the hyperpolarization decreases according to the apparent longitudinal relaxation
times while the sample is being transferred, which makes current hardware mainly suited for
13C NMR spectroscopy. Still, D-DNP has been successfully applied to cancer cell and plant
extracts at natural 13C abundance93 (Figure 6), and Bornet et al. demonstrated an excellent
repeatability (ca. 4%) for this method, making it compatible with the precision requirements of
metabolomics.94 In addition, while D-DNP is an irreversible experiment which is not
compatible with the time-incremented nature of conventional multi-dimensional NMR, 2D
spectra can be recorded by relying on UF 2D experiments, as shown by Dumez et al. on
extracts.93 Apart from this work at natural 13C abundance, Lerche and co-workers have
developed an elegant approach that relies on the incubation of the targeted biological material
(eg. cancer cells) prior to the D-DNP experiment.95 This approach has the double advantage
that it benefits from an enhanced sensitivity thanks to 13C labeling, while providing selective
information on metabolic pathways through the detection of a limited number of metabolites –
those which have incorporated the initial 13C labels. This strategy opens the way to
hyperpolarized fluxomics applications.
Considering the current limitations in terms of sample transfer between the polarizer and the
liquid-state spectrometer (several seconds), one can anticipate that D-DNP will mainly open
perspectives to enhance the sensitivity of 13C NMR metabolomics. But the technique is rapidly
improving,96 and the development of rapid dissolution and transfer systems should make D-
DNP compatible with the detection of hyperpolarized 1H spectra, opening considerable
perspectives for metabolomics and fluxomics.
Figure 6. Quaternary region of 13C NMR spectra of green tomato fruit pericarp extracts. (Top) Single-
scan 13C NMR spectrum of a 20 mg extract (prepared from 20 mg lyophilized ground tissue) recorded
with a single 90° pulse after D-DNP boosted by cross polarization. Th extract was first dissolved in
200 μL of a mixture of H2O/D2O/glycerol-d8 (1:4:5) doped with 50 mM TEMPOL, then polarized for
28 min at 1.2 K and 6.7 T, and finally dissolved with 5 mL of hot D2O and transferred to a 500 MHz
spectrometer equipped with a cryogenic probe. (Bottom) Conventional spectrum, obtained without
hyperpolarization, of an identical extract dissolved in 700 μL of D2O, recorded with 1024 scans (11 h
45 min) at 700 MHz using a cryogenic probe. Adapted with permission from Ref. 94. Copyright 2016
American Chemical Society.
11 h 45 min, no DNP
1 scan after 30 min DNP
Control sample1 scan after 30 min DNP
500 MHz
Tomato extract1 scan after 30 min DNP
500 MHz
Tomato extract1024 scans (11 h 45 min)
700 MHz, no DNP
*
*
Mal + Cit
GABAGln
180 175 170 165 [ppm]
Towards more accessible NMR metabolomics
In addition to its lower sensitivity, the limited accessibility of NMR is certainly the second
reason explaining that is it less widespread than MS in metabolomics and fluxomics
applications. This accessibility arises from complex reasons that combine the cost and
heaviness of the NMR equipment –often judged as prohibitive even though operating costs are
much lower than for MS– with its high level of technicity, including the need to handle
cryogenic fluids to fill superconducting magnets. Therefore, there is a major challenge in
making NMR more accessible, and several manufacturers have been tackling it since 2013 with
the development of compact NMR spectrometers.97 Such low-field magnets (1H resonance
between 40 to 100 MHz) are transportable (<100 kg), low-cost (<100 000 €) and rely on
permanent magnets that do not require any specific operation (apart from a well-regulated room
temperature).
Writing about such magnets –which have been initially developed for teaching and
reaction/process monitoring purposes– may seem in contradiction with the resolution and
sensitivity limitations of NMR metabolomics and fluxomics. Indeed, a 60 MHz magnet is –
regardless of probe homogeneity considerations– more than 30 times less sensitive than a 600
MHz one, and peaks are much more overlapped owing to the limited frequency range (10 ppm
corresponds to 600 Hz on a 60 MHz spectrometer, versus 6000 Hz on a 600 MHz spectrometer).
Therefore, there is little chance that such compact spectrometers could replace high-field NMR
instruments for the discovery of new biomarkers. However, when considering untargeted
approaches which aim at separating sample groups for classification purposes (diagnosis,
authentication…), fingerprinting strategies relying on the bucketing of the 1H NMR fingerprint
could contain enough information to provide the expected group separation. This is particularly
the case when sample amount is not limited, such as in extracts, food matrices or even urine
samples.
Such metabolomics classification approaches using benchtop NMR instruments have already
been reported in the recent literature. An impressive study was published in 2018 by Percival
et al., showing how a 60 MHz benchtop spectrometer could detect and quantify a dozen of
metabolites in urine and serum, with limits of detection of ca. 25 µM.98 Incorporated within a
classical metabolomics workflow, the benchtop method led to a very efficient group separation
between urine samples from type 2 diabetic patients and healthy controls. Other illustrations of
the potential of benchtop NMR metabolomics were reported in the so-called “foodomics” field,
such as the discrimination between beef versus horse meat99 or the detection of adulteration of
perilla oil with soybean oil.100 An application to the quality control of diesel fuel was also
recently reported.101 Although not belonging to metabolomics in the strictest sense of the word,
these profiling applications are very interesting, because they illustrate the potential of benchtop
NMR to make metabolomics approaches accessible to fields of science and industry where
NMR is not traditionally used.
The potential of benchtop NMR for metabolomics is still unexplored, and all the applications
mentioned above are less than 2 years old. Moreover, these applications relied on basic 1D 1H
NMR experiments, thus not taking advantage of the full pulse sequence programming
capabilities of NMR spectroscopy. In the last few years, the emergence of pulse programming
capabilities on benchtop instruments, associated with the implementation of gradient coils –a
basic ingredient of all modern pulse sequences– has made it possible to accelerate the
implementation of classical high-field tools for the characterization of complex mixtures. The
first UF 2D NMR spectra on a benchtop spectrometer were published by Gouilleux et al.,102
then the first DOSY and pure-shift experiments at low field were also reported.103 The
implementation of a gradient coil also allowed the implementation of advanced solvent
suppression methods.104 The combination of benchtop NMR with SABRE hyperpolarization
also opens promising perspectives to alleviate the sensitivity limitation of benchtop
instruments.105 Such tools have the potential to maximize the potential of benchtop NMR
metabolomics, and considering that their implementation is extremely recent, many interesting
stories remain to be written. Along this line, Gouilleux et al. demonstrated that UF 2D COSY
spectra of edible oils, recorded in 2.4 min on a 43 MHz benchtop spectrometer and processed
with multivariate analysis, provided a much better discrimination of the botanical origin of
edible oils than 1D spectra recorded in the same duration106 (Figure 7). This improved
performance of fast 2D NMR was attributed to the better separation of overlapping lipid
resonances. This result highlights the need for advanced pulse sequences to maximize the
capabilities of benchtop instruments for metabolomics.
Figure 7. Illustration of the potential of 2D experiments for the profiling of food samples with
benchtop NMR spectroscopy. (Top) Ultrafast 2D COSY spectrum recorded in 2.4 min on a sunflower
oil sample in non-deuterated chloroform. (Middle) PCA analysis obtained with such UF 2D NMR
experiments on 23 edible oil samples from different botanical origins. (Bottom) PCA on the same
sample set with standard 1D experiments and a variable bucketing approach. Reprinted from Ref. 106
with permission from Elsevier.
UF COSY, 2.4 min
Sunflower oil
43 MHz
Towards an improved identification of biomarkers
The identification of known and unknown biomarkers in biological samples is one of the major
challenges that both MS and NMR have to face in metabolomics. In MS, the challenge arises
from the huge number of features that can be detected –up to 30,000 in blood for instance107
and to the fact that a given feature does not correspond to a unique metabolite. In NMR, the
main bottleneck is to identify peaks that belong to the same compound within complex and
overlapped spectra patterns. Lots of efforts have recently been devoted to address this
challenge.6 They include the development of dedicated 1D and 2D NMR methods combined
with databases, as well as statistical methods based on correlations or ratio analysis.
NMR methods and databases
Most of the methods to better extract individual sub-spectra from mixtures rely on the
combination of dedicated 1D and 2D pulse sequences with spiking experiments (when
standards are available) and databases. In 1D NMR, selective TOCSY approaches have been
developed to make the identification and quantification of individual metabolites easier, by
helping to connect peaks which are part of the same spin system.108 This approach can even be
combined with HPLC fractionation to help identifying unknown compounds in the case of very
complex mixtures.109 But most identification approaches also rely on 2D NMR pulse sequences,
among which TOCSY and 1H-13C HSQC are the most popular. 2D spectra are increasingly
available in databases such as HMDB (Human Metabolome Database),110 BMRB (Biological
Magnetic Resonance Data Bank),111 MMCD (Madison Metabolomics Consortium Database)112
and PRIMe (Platform for RIKEN Metabolomics).113 Bruschweiler and co-workers proposed an
improved algorithm named COLMAR (Complex Mixture Analysis by NMR) which has been
made available on a web server and helps to identify metabolites from a database relying on
HSQC, TOCSY and HSQC-TOCSY spectra.114 In parallel of these approaches, complementary
efforts have focused on the use of 13C labeling to circumvent the sensitivity limitation of 13C
NMR in these identification workflows. For instance, the DemixC method is based on the
covariance processing of 13C-13C TOCSY spectra.115
Using the above mentioned strategies combining 1D and 2D spectra with spiking experiments
and databases, the NMR metabolomics community has obtained impressive results in terms of
identification. For instance, Gowda and Raftery identified nearly 70 metabolites in human blood
samples, 1/3 of which had not been previously reported.116 Wishart and co-workers managed
to identify 209 metabolites in human urine relying on the combined use of NMR and
databases.117 These impressive results highlight the performance of NMR as an essential
identification tool in metabolomics, and such approaches will certainly benefit from the
tremendous current advances in machine learning.
Correlation and ratio analysis methods
Correlation methods form an impressive set of approaches capable of identifying peaks that
belong to the same metabolite, and they have also contributed to maximize the potential of
NMR for the identification of metabolites in complex mixtures. The most widely used method
is the STOCSY (statistical correlation spectroscopy) which correlates the intensity variables in
a set of 1D spectra to generate a pseudo-two-dimensional NMR spectrum that displays the
correlation among the peak intensities across the whole sample.118 Metabolites can be identified
based on peaks showing the highest level of correlations. Research along this line is still very
active, with the recent development of several variants.119, 120
A slightly different approach has been recently published, that resembles the molecular network
approach which is increasingly popular in the analysis of MS metabolomics data.121 In their so-
called « maximal clique » method, Li et al. developed an automated algorithm to analyse
TOCSY spectra by representing peak connectivities as a mathematical graph, in which each
subgraph can be assigned to an individual spin system.122 This original method offers a way to
easily extract critical spin system information from 2D spectra.
A last family of approaches relying on ratio analysis were recently developed by Raftery and
co-workers to automate the extraction of relevant information from a set of NMR metabolomics
spectra. The initial method, called RANSY (ratio analysis of nuclear magnetic resonance
spectroscopy), identifies the peaks of individual metabolites by relying on the principles that
the intensity ratios from a given metabolite are fixed.123 It first requires defining a « driving »
peak belonging the compound of interest. Peak ratios derived from a set of NMR spectra are
then divided by the ratios’ standard deviations across a sample set to generate the individual
RANSY spectrum. Very recently, a derived version of this approach was described, called E-
RANSY (extractive ratio analysis NMR spectroscopy).124 In this approach, the NMR spectra of
metabolic extracts obtained at different pH conditions from the same biological sample are
compared through the ratio approach. Ratio methods (RANSY and E-RANSY) were shown to
be significantly more efficient than the correlation approaches. Figure 8 illustrate the potential
of such ratio methods in the case of urine.
Figure 8. Comparison of the results of ratio analysis and correlation analysis of either extracted urine
or intact urine spectra using the driving peak as indicated by the asterisk (*). The spectra shown are (a)
E-RANSY, (b) RANSY, (c) E-STOCSY, (d) STOCSY, and (e) the intact urine 1D 1H NMR spectrum.
The inset shows the structure of 4-hydroxyphenylacetic acid identified on the basis of E-RANSY.
Peaks in the E-RANSY spectrum are labeled with the corresponding protons as labeled in the structure
of the metabolite. For RANSY and STOCSY, intact urine NMR spectra were used; for E-RANSY and
E-STOCSY, ethyl acetate extracted urine NMR spectra were used. Reproduced with permission from
Ref. 124. Copyright 2019 Americal Chemical Society.
Combined NMR/MS strategies
The ultimate approach to an efficient identification of biomarkers would most likely rely on the
combination of several analytical techniques, typically NMR and MS, to maximize the
accessible structural information. In particular, accurate mass determination by MS can
significantly improve the structure elucidation process by NMR. That being said, there are not
many studies where both techniques have been employed synergystically, in particular due to
the difficulty to extract information from specific metabolites without relying on purification
steps. Some multivariate statistical analysis methods have been introduced that integrate NMR
and MS, but they do not provide molecular structures.125, 126
Significant efforts along this direction have been made by Bruschweiler and co-workers to
efficiently combine both analytical methods for an easier identification of metabolites. For the
identification of known metabolites (ie. those which are already present in databases), an
NMR/MS translator has been developed127 which first identifies candidate structures from 1D
and 2D NMR spectra associated with a database query, followed by the determination of the
m/z ratio for the possible ions, adducts and fragments for these candidates. The calculated m/z
ratios are then compared with the real mass spectrum to identify the structure of known
metabolites. When signals from unknown metabolites are highlighted, a second approach to
identify them is the SUMMIT MS/NMR approach, which works the other way around.128 The
SUMMIT method first identifies the possible chemical formulas for all mixture components
from accurate masses obtained by MS, and generates consistent candidate chemical structures
corresponding to these formulas. Then, the NMR spectra of these candidates are predicted and
compared with the experimental NMR spectra of the complex mixture to identify the structures
matching the information obtained from both analytical methods.
Conclusion
The take home message of this review is that liquid-state NMR of complex mixtures is currently
experiencing tremendous developments which have the potential to bring back NMR in the
foreground of metabolomics and fluxomics. Most of these developments aim at making NMR
more resolutive (fast 2D methods, pure-shift…), more sensitive (probes, hyperpolarization…),
but also to maximize the structure elucidation capabilities of NMR. Still, these advanced
methodological developments often come with a loss of the intrinsic characteristics of 1D
NMR, such as its absolute quantitative properties or its non-destructive character. But NMR
spectroscopists have shown great ability to deal with these drawbacks by suggesting clever
analytical approaches. Eventually, readers should also keep an eye on the rapidly evolving field
of benchtop NMR, since those portable instruments could make NMR profiling much more
broadly accessible within the next decade.
An additional message lies in the high complementarity between MS and NMR. While this
complementarity was highlighted in the structure elucidation section of this review, other
promising studies also recently highlighted such complementarity for the quantitative analysis
of samples with metabolomics relevance.129, 130 In untargeted metabolomics, multiple studies
have been showing the complementarity of the MS and NMR to solve specific biological
questions.3, 131 But even more promising results arise from the recent development of advanced
statistical approaches dedicated to the integration of analytical data from multiple platforms.132,
133 While NMR has a lot to bring to metabolomics and fluxomics, these recent works certainly
predict a bright future for multi-technique analytical workflows in the field.
Acknowledgements
The author is grateful to the numerous colleagues and students from the EBSI group at the
CEISAM research institute for their hard work and critical discussions over the last 10 years.
Special thanks to Dr. Jean-Nicolas Dumez for the critical reading of the manuscript. The
Corsaire metabolomics core facility is also acknowledged, as well as the Francophone
metabolomics and fluxomics family (RFMF, MetaboHUB and others). The author
acknowledges support of the European Research Council under the European Union’s Horizon
2020 research and innovation program (ERC Grant Agreement n° 814747/SUMMIT).
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